Md Mostafizur Rahman
Gianluca Demartini
ABSTRACT
The type of an entity is a key piece of information to understand what an entity is and how it relates to other entities mentioned in a document. Search engine result pages (SERPs) often surface facts and entity type information from a background Knowledge Graph (KG) in response to queries that carry a semantic information need. In a KG, an entity usually holds multiple type properties. It is then important to, given an entity in a KG, rank entity types attached to the entity by relevance to a certain user and information need as not always the most popular type is the most informative within a textual context.
In this paper we address the entity type ranking problem by means of KG embedding models. In our work, we show that entity type ranking can be seen as a special case of the KG completion problem. Embeddings can be learned from both the structural and probabilistic information of the entities. We propose a Representation Learning model for Type Ranking (RL-TRank) and the results of the structure embedding and the probabilistic embedding are combined to get the entity type ranking. Experimental results show that the accuracy of RL-TRank approaches outperform the state-of-the-art type ranking models while, at the same time, being more efficient and scalable.
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Index Terms
- Representation learning for entity type ranking
Index terms have been assigned to the content through auto-classification.
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